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Machine learning Classification problem to classify 7 types of dry beans based on morphological features only using different classifiers and a voting classifier.

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Dry Beans Classification

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In this project different machine learning algorithms were used to classify the most well-known 7 types of beans in Turkey; Barbunya, Bombay, Cali, Dermason, Horoz, Seker and Sira, depending ONLY on dimension and shape features of bean varieties with no external discriminatory features.

  • This code was part of a Kaggle competition and came in the 9th place out of 81 teams.

MLP, Xgboost, Catboost and LightGBM classifiers were trained and a final VotingClassifier is used resulting F1-score 0.956 on the training data, 0.935 on validation set and 0.938 on the final testing set.

Table of contents:

1-Dataset

2-EDA

3-Preprocessing

4-Model Training

5-Model Training

Dataset

The dataset provided in this project is obtained from UC Irvine Machine Learning Repository - Dry Bean Dataset.

  • Note: The data is already splitted with 80% - 20% ratio to training and testing sets respectively, so a part of the data is already separated for final testing and will use the training set for train and validation.

EDA

  • Exploring the dataset, getting summary statistics and checking for null values and duplicates and there weren't any.
  • Graphical representations:
    1- Count plot the labels column show the distribution of all classes that showed a slight imbalance but it doesn't affect and no need to handle.
    2- Histogram of numerical features, some distributions have long tails, skewed and most are bi-modal which means that some classes are quite distinct from others.
    3- Boxplot shows that the "Bombay" & "Horoz" classes are distinct from other classes and that there are some minimal outliers in some features.
    4- The Pearson linear correlation shows that there are lots of highly correlated features.

Preprocessing

1- Label Encoding the categorical target labels with values from 0 to 6.
2- Train-Validation split the training dataset with 95% - 5% ratio.
Now, we have the training set:
3- Remove outliers from some features with certain threshold.
4- Feature scaling using StadardScalar()

Model Training

SVC, MLP, Xgboost, Catboost and LightGBM classifiers were trained on the dataset separately.

  • RandomizedSearchCV was used previously in hyperparameter optimization for the models and the best parameters are used directly in this code.
  • F1-score and Confusion Matrix are used to evaluate each model's performance.

    Finally, Voting classifier with 'soft' voting is used with the four best models; MLP, Xgboost, Catboost and LightGBM.

Setup

To run this project, install:
1- numpy library
2- matplotlib library
3- pandas library
4- seaborn library
5- sklearn library
6- scipy library
7- os library

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Machine learning Classification problem to classify 7 types of dry beans based on morphological features only using different classifiers and a voting classifier.

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